📚 Livros

Última atualização: 22/08/2025

Sumário

📘Machine Learning

📘 Deep Learning

📘 LLMs, RAG e IA Generativa

📘 Estatística

📘 Programação em R

📘 Machine Learning

N°	Título	Link
1	Aprendizado de Máquina: Uma Abordagem Estatística – Izbicki & Santos	[Link]
2	Inteligência Artificial e Aprendizagem de Máquina – Oscar Gabriel Filho	[Link]
3	Introdução ao Aprendizado de Máquina para Análise de Sobrevivência – Ara et al.	[Link]
4	Uma Introdução à Machine Learning – David Menotti (UFPR)	[Link]
5	A Course in Machine Learning – Hal Daumé III	[Link]
6	Mathematics for Machine Learning – Beginners – Mueller & Massaron	[Link]
7	ML Cheatsheet for Beginners – Jupyter Compilation	[Link]
8	Decision Tree Regressor Basics – Nikita Prasad	[Link]
9	Tutorials on Machine Learning & Deep Learning – Compilação técnica	[Link]
10	Algorithmic Mathematics in Machine Learning – Bohn, Garcke, Griebel	[Link]
11	Engineering MLOps – Emmanuel Raj	[Link]
12	Interpretable Machine Learning with Python – Serg Masís	[Link]
13	Machine Learning Applications: From Computer Vision to Robotics – Chatterjee & Zalte (Eds.)	[Link]
14	Machine Learning Using R – Brett Lantz	[Link]
15	A Minimal rTorch Book	https://f0nzie.github.io/rtorch-minimal-book/
16	Applied Machine Learning for Tabular Data	https://aml4td.org/
17	Applied Machine Learning Using mlr3 in R	https://mlr3book.mlr-org.com/
18	Behavior Analysis with Machine Learning Using R	https://enriquegit.github.io/behavior-free/index.html#
19	Data Science: Theories, Models, Algorithms, and Analytics	https://srdas.github.io/MLBook/
20	Deep Learning and Scientific Computing with R torch	https://skeydan.github.io/Deep-Learning-and-Scientific-Computing-with-R-torch/
21	Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels	https://emlwr.org/
22	Explanatory Model Analysis	https://pbiecek.github.io/ema/
23	Feature Engineering A-Z	https://feaz-book.com/
24	Feature Engineering and Selection: A Practical Approach for Predictive Models	http://www.feat.engineering/index.html
25	Hands-On Machine Learning with R	https://bradleyboehmke.github.io/HOML/
26	Interpretable Machine Learning	https://leanpub.com/interpretable-machine-learning
27	Lightweight Machine Learning Classics with R	https://lmlcr.gagolewski.com/
28	Machine Learning-based Causal Inference Tutorial	https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/
29	Machine Learning for Factor Investing	http://www.mlfactor.com/
30	Mathematics and Programming for Machine Learning with R: From the Ground Up	https://www.amazon.com/Mathematics-Programming-Machine-Learning-Ground-ebook-dp-B08JHDCX9Y/dp/B08JHDCX9Y
31	Model-Based Clustering, Classification and Density Estimation Using mclust in R	https://mclust-org.github.io/mclust-book/
32	Neural Cryptography Using Keras in R	https://www.statswithr.com/neural-cryptography-using-keras-in-r
33	Neural Networks with Keras in R: A QuickStart Guide	https://www.statswithr.com/neural-networks-with-keras-in-r-a-quickstart-guide
34	sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series	https://e-sensing.github.io/sitsbook/
35	Supervised Machine Learning for Text Analysis in R	https://smltar.com/
36	Surrogates – Gaussian Process Modeling, Design and Optimization for the Applied Sciences	https://bookdown.org/rbg/surrogates/
37	The caret Package	https://topepo.github.io/caret/index.html
38	The Hitchhiker’s Guide to Responsible Machine Learning	https://betaandbit.github.io/RML/
39	Tidy Modeling with R	https://www.tmwr.org/

📘 Deep Learning

Nº	Título	Link
1	Deep Learning for the Life Sciences – Ramsundar, Eastman, Walters, Pande	[Link]
2	Time Series Analysis & Machine Learning for Predictive Modeling – Rathod et al.	[Link]
3	Tutorials on Machine Learning & Deep Learning – Compilação técnica	[Link]

📘 LLMs, RAG e IA Generativa

Nº	Título	Link
1	Unlocking Data with Generative AI and RAG – Keith Bourne	[Link]
2	Building LLMs for Production – Sinan Ozdemir	[Link]
3	Mastering LLM Applications with LangChain and Hugging Face – Eric Sarrion, Julien Simon	[Link]
4	Generative AI for Cloud Solutions Architect – Maddie Schieferstein	[Link]

📘 Estatística

🔹 Fundamentos e Introduções

Nº	Título	Link
1	An Introduction to Statistical and Data Sciences via R	https://moderndive.com/
2	An Introduction to Statistical Learning	https://www.statlearning.com/
3	Introduction to Modern Statistics	https://openintro-ims.netlify.app/
4	OpenIntro Statistics	https://leanpub.com/openintro-statistics
5	Statistics (The Easier Way) With R, 3rd Ed.	https://amzn.to/3b9ha8s
6	Statistics and Data with R	https://www.wiley.com/en-us/Statistics+and+Data+with+R%3A+An+Applied+Approach+Through+Examples-p-9780470758052
7	Answering questions with data	https://crumplab.github.io/statistics/

🔹 Bayes e Probabilidade

Nº	Título	Link
1	Bayes rules!	https://www.bayesrulesbook.com/
2	An Introduction to Bayesian Data Analysis for Cognitive Science	https://vasishth.github.io/bayescogsci/book/
3	An Introduction to Bayesian Reasoning and Methods	https://bookdown.org/kevin_davisross/bayesian-reasoning-and-methods/
4	Doing Bayesian Data Analysis in brms and the tidyverse	https://bookdown.org/content/3686/
5	Bayesian analysis of capture-recapture data	https://oliviergimenez.github.io/banana-book/index.html
6	Introduction to Empirical Bayes	https://drob.gumroad.com/l/empirical-bayes
7	Probability and Bayesian Modeling	https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html
8	Statistical Rethinking	https://xcelab.net/rm/statistical-rethinking/
9	Statistical Rethinking with brms, ggplot2, and the tidyverse	https://bookdown.org/content/4857/
10	Using R for Bayesian Spatial and Spatio-Temporal Health Modeling	https://www.routledge.com/…

🔹 Regressão e GLM

Nº	Título	Link
1	Advanced Regression Methods – Companion to BER642	https://bookdown.org/chua/ber642_advanced_regression/
2	Analysing Data using Linear Models	https://bookdown.org/pingapang9/linear_models_bookdown/
3	Beyond Multiple Linear Regression	https://bookdown.org/roback/bookdown-BeyondMLR/
4	Flexible Regression Models	https://discdown.org/flexregression/
5	Handbook of Regression Modeling in People Analytics	http://peopleanalytics-regression-book.org/index.html
6	Introduction to Regression Analysis in R	https://www.kellerbiostat.com/introregression/
7	Regression and Other Stories	https://avehtari.github.io/ROS-Examples/
8	The Hitchhiker’s Guide to Linear Models	https://leanpub.com/linear-models-guide

🔹 Inferência

Nº	Título	Link
1	Common statistical tests are linear models	https://steverxd.github.io/Stat_tests/
2	Statistical inference for data science	https://leanpub.com/LittleInferenceBook
3	Foundations of Statistics with R	https://mathstat.slu.edu/~speegle/_book/preface.html
4	Statistical Thinking in the 21st Century	https://statsthinking21.github.io/statsthinking21-core-site/

🔹 Longitudinal e Mistos

Nº	Título	Link
1	Applied longitudinal data analysis in brms and the tidyverse	https://bookdown.org/content/4253/
2	Mixed Models with R	https://m-clark.github.io/mixed-models-with-R/

🔹 Meta-análise e Poder

Nº	Título	Link
1	Doing meta-analysis with R	https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/
2	Power Analysis with Superpower	https://aaroncaldwell.us/SuperpowerBook/index.html
3	Introduction to Mediation, Moderation, and Conditional Process Analysis	https://bookdown.org/content/b472c7b3-ede5-40f0-9677-75c3704c7e5c/

🔹 Causalidade

Nº	Título	Link
1	Causal Inference in R	https://www.r-causal.org
2	Causal Inference: The Mixtape	https://mixtape.scunning.com/
3	The Effect: An Introduction to Research Design and Causality	https://theeffectbook.net/

🔹 Séries Temporais e Multivariada

Nº	Título	Link
1	A Little Book of R for Time Series	https://a-little-book-of-r-for-time-series.readthedocs.io
2	A Little Book of R for Multivariate Analysis	https://little-book-of-r-for-multivariate-analysis.readthedocs.io

🔹 Didáticos e Catálogos

Nº	Título	Link
1	End-to-End Solved Problems With R	https://amzn.to/2EREAn2
2	Model Estimation by Example	https://m-clark.github.io/models-by-example/
3	Library of Statistical Techniques	https://lost-stats.github.io/
4	ISLR tidymodels Labs	https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html
5	Marginal Effects Zoo	https://marginaleffects.com
6	Teacups, Giraffes and Statistics	https://tinystats.github.io/teacups-giraffes-and-statistics/index.html
7	The Saga of PLS	https://sagaofpls.github.io
8	Translating Stata to R	https://stata2r.github.io/

🔹 Áreas Aplicadas

Nº	Título	Link
1	Building energy statistical modelling	https://buildingenergygeeks.org/index.html
2	Modern Statistical Methods for Astronomy	https://www.cambridge.org/…
3	Spatio-Temporal Statistics with R	https://spacetimewithr.org/
4	Surrogates – Gaussian process modeling	https://bookdown.org/rbg/surrogates/
5	Partial Least Squares Structural Equation Modeling (PLS-SEM)	https://link.springer.com/book/10.1007/978-3-030-80519-7
6	The Grammar of Experimental Designs	https://emitanaka.org/edibble-book/index.html
7	One Way ANOVA with R	https://bcdudek.net/anova/oneway_anova_basics.pdf
8	Data Analytics	https://discdown.org/dataanalytics/
9	R for Data Analytics	https://rforanalytics.com/
10	A Business Analyst’s Introduction to Business Analytics	https://www.causact.com/

📘 Programação em R

Nº	Título	Link
1	R para Ciência de Dados (2ª edição) – Português	[Link]
2	Ciência de Dados em R – Cursos R	[Link]
3	Introdução à Linguagem R: seus fundamentos e sua prática – Pedro Duarte Faria	[Link]
4	Statistical Inference via Data Science – Ismay & Kim	[Link]
5	ggplot2: Elegant Graphics for Data Science – Hadley Wickham	[Link]
6	R for Data Science (1ª ed): Exercise Solutions – Jeffrey B. Arnold	[Link]
7	Data Manipulation in R – Steph Locke	[Link]

--- title: "Jennifer Lopes" toc: true # Adiciona um índice no relatório format: html: css: ["styles.css"] # Customização com css toc: true embed-resources: true code-fold: false # code-fold: true para "ocultar" o bloco de códigos code-tools: true #code-tools: true para ativar ferramentas de interação com os códigos # number-sections: true # Número de seções/subseções title-block-banner: "#6b44e8" # Pode ser 'true', uma cor ou imagem code-annotations: hover # Para anotações em códigos --- # 📚 Livros **Última atualização: 22/08/2025** ## Sumário 📘Machine Learning 📘 Deep Learning 📘 LLMs, RAG e IA Generativa 📘 Estatística 📘 Programação em R ## 📘 Machine Learning ![](images/clipboard-2482400552.png){width="290"} | N° | Título | Link | |------------------|-------------------------------|------------------------| | 1 | Aprendizado de Máquina: Uma Abordagem Estatística – Izbicki & Santos | \[Link\] | | 2 | Inteligência Artificial e Aprendizagem de Máquina – Oscar Gabriel Filho | \[Link\] | | 3 | Introdução ao Aprendizado de Máquina para Análise de Sobrevivência – Ara et al. | \[Link\] | | 4 | Uma Introdução à Machine Learning – David Menotti (UFPR) | \[Link\] | | 5 | A Course in Machine Learning – Hal Daumé III | \[Link\] | | 6 | Mathematics for Machine Learning – Beginners – Mueller & Massaron | \[Link\] | | 7 | ML Cheatsheet for Beginners – Jupyter Compilation | \[Link\] | | 8 | Decision Tree Regressor Basics – Nikita Prasad | \[Link\] | | 9 | Tutorials on Machine Learning & Deep Learning – Compilação técnica | \[Link\] | | 10 | Algorithmic Mathematics in Machine Learning – Bohn, Garcke, Griebel | \[Link\] | | 11 | Engineering MLOps – Emmanuel Raj | \[Link\] | | 12 | Interpretable Machine Learning with Python – Serg Masís | \[Link\] | | 13 | Machine Learning Applications: From Computer Vision to Robotics – Chatterjee & Zalte (Eds.) | \[Link\] | | 14 | Machine Learning Using R – Brett Lantz | \[Link\] | | 15 | A Minimal rTorch Book | <https://f0nzie.github.io/rtorch-minimal-book/> | | 16 | Applied Machine Learning for Tabular Data | <https://aml4td.org/> | | 17 | Applied Machine Learning Using mlr3 in R | <https://mlr3book.mlr-org.com/> | | 18 | Behavior Analysis with Machine Learning Using R | <https://enriquegit.github.io/behavior-free/index.html#> | | 19 | Data Science: Theories, Models, Algorithms, and Analytics | <https://srdas.github.io/MLBook/> | | 20 | Deep Learning and Scientific Computing with R torch | <https://skeydan.github.io/Deep-Learning-and-Scientific-Computing-with-R-torch/> | | 21 | Efficient Machine Learning with R: Low-Compute Predictive Modeling with tidymodels | <https://emlwr.org/> | | 22 | Explanatory Model Analysis | <https://pbiecek.github.io/ema/> | | 23 | Feature Engineering A-Z | <https://feaz-book.com/> | | 24 | Feature Engineering and Selection: A Practical Approach for Predictive Models | <http://www.feat.engineering/index.html> | | 25 | Hands-On Machine Learning with R | <https://bradleyboehmke.github.io/HOML/> | | 26 | Interpretable Machine Learning | <https://leanpub.com/interpretable-machine-learning> | | 27 | Lightweight Machine Learning Classics with R | <https://lmlcr.gagolewski.com/> | | 28 | Machine Learning-based Causal Inference Tutorial | <https://bookdown.org/stanfordgsbsilab/ml-ci-tutorial/> | | 29 | Machine Learning for Factor Investing | <http://www.mlfactor.com/> | | 30 | Mathematics and Programming for Machine Learning with R: From the Ground Up | <https://www.amazon.com/Mathematics-Programming-Machine-Learning-Ground-ebook-dp-B08JHDCX9Y/dp/B08JHDCX9Y> | | 31 | Model-Based Clustering, Classification and Density Estimation Using mclust in R | <https://mclust-org.github.io/mclust-book/> | | 32 | Neural Cryptography Using Keras in R | <https://www.statswithr.com/neural-cryptography-using-keras-in-r> | | 33 | Neural Networks with Keras in R: A QuickStart Guide | <https://www.statswithr.com/neural-networks-with-keras-in-r-a-quickstart-guide> | | 34 | sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series | <https://e-sensing.github.io/sitsbook/> | | 35 | Supervised Machine Learning for Text Analysis in R | <https://smltar.com/> | | 36 | Surrogates – Gaussian Process Modeling, Design and Optimization for the Applied Sciences | <https://bookdown.org/rbg/surrogates/> | | 37 | The caret Package | <https://topepo.github.io/caret/index.html> | | 38 | The Hitchhiker’s Guide to Responsible Machine Learning | <https://betaandbit.github.io/RML/> | | 39 | Tidy Modeling with R | <https://www.tmwr.org/> | ## 📘 Deep Learning | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Deep Learning for the Life Sciences – Ramsundar, Eastman, Walters, Pande | \[Link\] | | 2 | Time Series Analysis & Machine Learning for Predictive Modeling – Rathod et al. | \[Link\] | | 3 | Tutorials on Machine Learning & Deep Learning – Compilação técnica | \[Link\] | ## 📘 LLMs, RAG e IA Generativa | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Unlocking Data with Generative AI and RAG – Keith Bourne | \[Link\] | | 2 | Building LLMs for Production – Sinan Ozdemir | \[Link\] | | 3 | Mastering LLM Applications with LangChain and Hugging Face – Eric Sarrion, Julien Simon | \[Link\] | | 4 | Generative AI for Cloud Solutions Architect – Maddie Schieferstein | \[Link\] | ## 📘 Estatística ### 🔹 Fundamentos e Introduções | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | An Introduction to Statistical and Data Sciences via R | <https://moderndive.com/> | | 2 | An Introduction to Statistical Learning | <https://www.statlearning.com/> | | 3 | Introduction to Modern Statistics | <https://openintro-ims.netlify.app/> | | 4 | OpenIntro Statistics | <https://leanpub.com/openintro-statistics> | | 5 | Statistics (The Easier Way) With R, 3rd Ed. | <https://amzn.to/3b9ha8s> | | 6 | Statistics and Data with R | <https://www.wiley.com/en-us/Statistics+and+Data+with+R%3A+An+Applied+Approach+Through+Examples-p-9780470758052> | | 7 | Answering questions with data | <https://crumplab.github.io/statistics/> | ### 🔹 Bayes e Probabilidade | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Bayes rules! | <https://www.bayesrulesbook.com/> | | 2 | An Introduction to Bayesian Data Analysis for Cognitive Science | <https://vasishth.github.io/bayescogsci/book/> | | 3 | An Introduction to Bayesian Reasoning and Methods | <https://bookdown.org/kevin_davisross/bayesian-reasoning-and-methods/> | | 4 | Doing Bayesian Data Analysis in brms and the tidyverse | <https://bookdown.org/content/3686/> | | 5 | Bayesian analysis of capture-recapture data | <https://oliviergimenez.github.io/banana-book/index.html> | | 6 | Introduction to Empirical Bayes | <https://drob.gumroad.com/l/empirical-bayes> | | 7 | Probability and Bayesian Modeling | <https://bayesball.github.io/BOOK/probability-a-measurement-of-uncertainty.html> | | 8 | Statistical Rethinking | <https://xcelab.net/rm/statistical-rethinking/> | | 9 | Statistical Rethinking with brms, ggplot2, and the tidyverse | <https://bookdown.org/content/4857/> | | 10 | Using R for Bayesian Spatial and Spatio-Temporal Health Modeling | <https://www.routledge.com/>... | ### 🔹 Regressão e GLM | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Advanced Regression Methods – Companion to BER642 | <https://bookdown.org/chua/ber642_advanced_regression/> | | 2 | Analysing Data using Linear Models | <https://bookdown.org/pingapang9/linear_models_bookdown/> | | 3 | Beyond Multiple Linear Regression | <https://bookdown.org/roback/bookdown-BeyondMLR/> | | 4 | Flexible Regression Models | <https://discdown.org/flexregression/> | | 5 | Handbook of Regression Modeling in People Analytics | <http://peopleanalytics-regression-book.org/index.html> | | 6 | Introduction to Regression Analysis in R | <https://www.kellerbiostat.com/introregression/> | | 7 | Regression and Other Stories | <https://avehtari.github.io/ROS-Examples/> | | 8 | The Hitchhiker’s Guide to Linear Models | <https://leanpub.com/linear-models-guide> | ### 🔹 Inferência | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Common statistical tests are linear models | <https://steverxd.github.io/Stat_tests/> | | 2 | Statistical inference for data science | <https://leanpub.com/LittleInferenceBook> | | 3 | Foundations of Statistics with R | <https://mathstat.slu.edu/~speegle/_book/preface.html> | | 4 | Statistical Thinking in the 21st Century | <https://statsthinking21.github.io/statsthinking21-core-site/> | ### 🔹 Longitudinal e Mistos | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Applied longitudinal data analysis in brms and the tidyverse | <https://bookdown.org/content/4253/> | | 2 | Mixed Models with R | <https://m-clark.github.io/mixed-models-with-R/> | ### 🔹 Meta-análise e Poder | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Doing meta-analysis with R | <https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/> | | 2 | Power Analysis with Superpower | <https://aaroncaldwell.us/SuperpowerBook/index.html> | | 3 | Introduction to Mediation, Moderation, and Conditional Process Analysis | <https://bookdown.org/content/b472c7b3-ede5-40f0-9677-75c3704c7e5c/> | ### 🔹 Causalidade | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Causal Inference in R | <https://www.r-causal.org> | | 2 | Causal Inference: The Mixtape | <https://mixtape.scunning.com/> | | 3 | The Effect: An Introduction to Research Design and Causality | <https://theeffectbook.net/> | ### 🔹 Séries Temporais e Multivariada | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | A Little Book of R for Time Series | <https://a-little-book-of-r-for-time-series.readthedocs.io> | | 2 | A Little Book of R for Multivariate Analysis | <https://little-book-of-r-for-multivariate-analysis.readthedocs.io> | ### 🔹 Didáticos e Catálogos | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | End-to-End Solved Problems With R | <https://amzn.to/2EREAn2> | | 2 | Model Estimation by Example | <https://m-clark.github.io/models-by-example/> | | 3 | Library of Statistical Techniques | <https://lost-stats.github.io/> | | 4 | ISLR tidymodels Labs | <https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/index.html> | | 5 | Marginal Effects Zoo | <https://marginaleffects.com> | | 6 | Teacups, Giraffes and Statistics | <https://tinystats.github.io/teacups-giraffes-and-statistics/index.html> | | 7 | The Saga of PLS | <https://sagaofpls.github.io> | | 8 | Translating Stata to R | <https://stata2r.github.io/> | ### 🔹 Áreas Aplicadas | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | Building energy statistical modelling | <https://buildingenergygeeks.org/index.html> | | 2 | Modern Statistical Methods for Astronomy | <https://www.cambridge.org/>... | | 3 | Spatio-Temporal Statistics with R | <https://spacetimewithr.org/> | | 4 | Surrogates – Gaussian process modeling | <https://bookdown.org/rbg/surrogates/> | | 5 | Partial Least Squares Structural Equation Modeling (PLS-SEM) | <https://link.springer.com/book/10.1007/978-3-030-80519-7> | | 6 | The Grammar of Experimental Designs | <https://emitanaka.org/edibble-book/index.html> | | 7 | One Way ANOVA with R | <https://bcdudek.net/anova/oneway_anova_basics.pdf> | | 8 | Data Analytics | <https://discdown.org/dataanalytics/> | | 9 | R for Data Analytics | <https://rforanalytics.com/> | | 10 | A Business Analyst’s Introduction to Business Analytics | <https://www.causact.com/> | ## 📘 Programação em R | Nº | Título | Link | |------------------------|------------------------|------------------------| | 1 | R para Ciência de Dados (2ª edição) – Português | \[Link\] | | 2 | Ciência de Dados em R – Cursos R | \[Link\] | | 3 | Introdução à Linguagem R: seus fundamentos e sua prática – Pedro Duarte Faria | \[Link\] | | 4 | Statistical Inference via Data Science – Ismay & Kim | \[Link\] | | 5 | ggplot2: Elegant Graphics for Data Science – Hadley Wickham | \[Link\] | | 6 | R for Data Science (1ª ed): Exercise Solutions – Jeffrey B. Arnold | \[Link\] | | 7 | Data Manipulation in R – Steph Locke | \[Link\] |